Benchmark Forecasts

Prepared for

WIPO-OECD Workshop on Statistics in the Patent Field,11-12 October 2004, Geneva, Switzerland

By Frederick L. Joutz

Research Program on ForecastingDepartment of Economics

The George Washington UniversityWashington, DC 20052

bmark@gwu.edu

Forecasting Patent Applications at the

European Patent Office:

A Bottom-Up Versus Top-Down Approach

Acknowledgements: This presentation benefited from helpful comments and suggestions by Peter Hingley, Marc Nicolas, and and Costas Mastrogianis. Any errors or omissions are my own. All opinions are mine and independent of the USPTO.

Benchmark Forecasts

Overview

This paper presents preliminary results on forecasting patent applications at the European Patent Office using annual data.

A two step framework is used in the modeling.First FilingsSecondary or Subsequent filings

Two Models are developed.– An aggregate model– A disaggregate regional model (Europe, Japan, US, and

Other)

Benchmark Forecasts

Overview

Forecasting patent filings is one of the important issues of the Trilateral Statistical Working Group, WIPO, and the OECD.

TSWG, composed of the EPO, JPO, and USPTO. The three offices meet at least once a year to discuss this issue among a host of other patenting issues.

TSWG has been holding annual meetings since 1992. The members treat forecasting as an important

exercise for planning future resource and manpower requirements and revenues.

This paper builds on previous research among the TSWG participants and a recent paper by Hingley and Nicolas (2004).

Benchmark Forecasts

A Theoretical Model of Patent Application Filing

Patents protect more than just intellectual property; they are an intrinsic component of the larger economic picture. This occurs through the process of innovation, technological and scientific change and economic productivity and growth. The process is the result of the demand for and production of “new” knowledge.

Schmookler (1954) - industrial invention is economically caused. In his view invention is driven by the interaction of supply and

demand forces. Scherer (1983) Pavitt (1982), Hall, Griliches, and Hausman (1986)

relationship between R&D effort and patent activities although primarily at the firm level.

Griliches (1989). Adams, Kim, Joutz, Trost, and Mastrogianis (1997) Eaton and Kortum (1996 and 1998) and Gardner and Joutz (1996)

Benchmark Forecasts

A Theoretical Model of Patent Application Filing

The most recent advancement of the endogenous growth theory has been the emergence of R&D-based models of growth in the seminal papers of Romer (1990), Grossman and Helpman (1991a, 1991b) and Aghion and Howitt (1992).

This class of models agrees with the neoclassical Solow model that capital broadly defined is subject to diminishing returns, and hence the accumulation of capital does not sustain growth in the long run.

Instead, technological progress is the source of sustained long run growth in both types of models.

The point of departure lies in the way technological progress is viewed. In the neoclassical model, technology evolves exogenously.

R&D- based models, the evolution of technology is explicitly and formally modeled as an endogenous process. Technological progress occurs as profit-maximizing firms invest in advanced technologies, and is promoted by the allocation of more productive resources towards R&D.

Benchmark Forecasts

A Theoretical Model of Patent Application Filing

The model involves four variables: Output (Y), capital (K), labor (L), and technology or knowledge (A).

There are two sectors: a goods- producing sector where output is produced, and an R&D sector where additions to the stock of knowledge are made. Labor can be freely allocated to either of the two sectors, to produce output (LY) or to produce new knowledge (LA).

Hence, the economy is subject to the following resource constraint LY + LA = L, where L represents the total amount of labor in the economy.

Specifically, output is produced according to the following production function:

1)()1( YALKY

Benchmark Forecasts

A Theoretical Model of Patent Application Filing

The production function approach to knowledge in is the underpinning of the long-term modeling framework

Research labor input is replaced by R&D expenditures as a measure of research effort primarily for data reasons.

The production function concept is used in a long-term context for generation of new knowledge. is represented by patent application filings and the level of is calculated as the stock of historical patents

t t A t AA A L A RD

Benchmark Forecasts

0

40000

80000

120000

160000

200000

80 82 84 86 88 90 92 94 96 98 00 02

Ap

plic

atio

ns

Total Filings at the EPO

Benchmark Forecasts

0

10000

20000

30000

40000

50000

60000

70000

80000

80 82 84 86 88 90 92 94 96 98 00 02

F_EPF_JP

F_OTF_US

Applic

atio

ns

Patent Filings at the EPO

Benchmark Forecasts

0

10

20

30

40

50

60

70

80 82 84 86 88 90 92 94 96 98 00 02

SHF_EPSHF_JP

SHF_USSHF_OT

perc

ent

EPO Filings - Regional Shares

Benchmark Forecasts

0

500000

1000000

1500000

2000000

2500000

3000000

1965 1970 1975 1980 1985 1990 1995 2000

AKD_US AKT_US

Stock of Knowledge - USP

aten

ts w

/ 7%

dep

.

Benchmark Forecasts

0

1000000

2000000

3000000

4000000

5000000

1965 1970 1975 1980 1985 1990 1995 2000

AKD_JP AKT_JP

Pat

ents

w/

7% d

ep.

Stock of Knowledge - Japan

Benchmark Forecasts

0

40000

80000

120000

160000

200000

240000

280000

1970 1975 1980 1985 1990 1995 2000

RD_EU RD_JP RD_US

Research and Development Expenditures - Europe, Japan, and US$

19

95

PP

P

Benchmark Forecasts

The Modeling Procedure Inventors typically first file a patent application in their home country.

The first filing represents an indicator of innovative activity. Patent protection on an international scale, perhaps based on

preliminary searches, is sought about a year later. The preferred route is through an international or supranational procedure to reduce the duplication costs. Currently the European Patent Organization has 31 contracting member countries. This route is referred to as a subsequent or secondary filing.

The forecasting problem is complicated by the fact that there are multiple routes for patent protection applications. Inventors have the option of filing nationally, through the European system, and the International PCT route administered through the World Intellectual Property Organization.

However, the primary work load of searches and preliminary examinations from the PCT applications is performed through nine patent offices. The EPO is one of the most important offices authorized or designated to perform this work. This has become increasingly popular as over 90% of the European contracting states are selected when using the PCT route.

Benchmark Forecasts

.0

.1

.2

.3

.4

.5

.6

80 82 84 86 88 90 92 94 96 98 00 02

FEPDOM_EP FEPDOM_JP FEPDOM_US

Filings at EPO Filings after Domestic Filings with 1 Year LagS

hare

Benchmark Forecasts

The Modeling Procedure

The model framework proceeds in two stages. See Hingley and Nicolas (2004) for a further exposition of this framework.

In the first stage non-EPO and EPC patent applications are filed domestically.

The model specification is ADL(p,p), autoregressive distributed lag model based on economic growth theory and the knowledge production function.

0 1 1 1 11 1 1 1

p p p p

t t i t i t i t i ti i i i

DomFil DomFil AK RD RGDP

Benchmark Forecasts

The Modeling Procedure These domestic or “first” filings are a strong indicator of

subsequent filings at the EPO and used in the second stage. The specification is similar.

0 1 11 1

1 11 1

p p

t t i t ii i

p p

t i t i ti i

SEPOFil DomFil SEPOFil

EPOFil ERGDP u

•Subsequent (or secondary) filings at the EPO are a function of •past domestic filings, •previous filings at the EPO, •the size of the EPO market, and •economic activity in Europe.

Benchmark Forecasts

1

1

1

, , ,

, , ,

, , ,

US US US US USt US t i t t i t i

JP JP JP JP JPt JP t i t t i t i

EU EU EU EU EUt EU t i t t i t i

Knowledge Production andDomestic Filing

DomFil f DomFil AK RD GDP

DomFil f DomFil AK RD GDP

DomFil f DomFil AK RD GDP

1

1

1

( )

, , ,

, , ,

, ,

US US US Tot USt US t i t t i t i

JP JP JP Tot JPt JP t i t t i t i

EU EU EUt EU t i t

Subsequent Filings at the EPO

SEPOFil f SEPOFil DomFil EPOFil GDP

SEPOFil f SEPOFil DomFil EPOFil GDP

SEPOFil f SEPOFil DomFil E

,

, ,

Tot EUt i t i

OT OT Tot EUt OT t i t i t i

Tot EU JP US OTt t t t t

POFil GDP

EPOFil f SEPOFil EPOFil GDP

EPOFil EPOFil EPOFil EPOFil EPOFil

Benchmark Forecasts

The Domestic Filing Model - US

Specific model of LFDOM_US, 1968 - 2003

Coeff StdError t-value Constant 22.30339 5.68230 3.925 LFDOM_US_1 2.50788 0.58479 4.289 LFDOM_US_2 0.41380 0.17107 2.419 LFDOM_US_3 0.40580 0.15110 2.686 LRD3_US_1 0.80166 0.24127 3.323 LRD3_US_3 -1.29514 0.34318 -3.774 LAKD_US_1 -17.30588 5.35807 -3.230 LAKD_US_2 13.08304 4.50410 2.905 LAKD_US_4 0.71821 0.41603 1.726 dp9596 0.12257 0.02417 5.072 Trend 0.05670 0.01446 3.921

Benchmark Forecasts

The Domestic Filing Model - US

RSS 0.02714 sigma 0.03295 LogLik 129.42304 AIC -6.57906 R^2 0.99424 Radj^2 0.99193 HQ -6.41018 SC -6.09520 T 36 p 11 value prob Chow(1986:1) 3.1522 0.0515 Chow(2000:1) 0.0387 0.9896 AR 1-4 test 1.4191 0.2623 ARCH 1-4 test 0.0876 0.9851 hetero test 20.9696 0.3989

Benchmark Forecasts

The Domestic Filing Model - USDynamic analysis – LongRun Coefficients

LAKD_US 1.5058 >>> Greater than Unity SE 0.0712 LRD3_US 0.2120 >>> Elasticity .2 SE 0.0573

dp9596 -0.0527 SE 0.0196Constant -9.5826 SE 0.9904Trend -0.0244 SE 0.0039

Benchmark Forecasts

The EPO Total Model Specific model of LF_TOT, 1982 - 2001

Coeff StdError t-value t-prob Constant 7.83067 3.16504 2.474 0.0268 LF_TOT_2 0.78506 0.05666 13.855 0.0000 LAKT_US 3.05625 0.31196 9.797 0.0000 lrd31_eu_1 0.62121 0.08990 6.910 0.0000 lrd31_eu_2 -0.38245 0.07114 -5.376 0.0001 LGDP3_EU_1 -5.88867 0.98197 -5.997 0.0000

RSS 0.00796 sigma 0.02384 LogLik 78.29397 AIC -7.22940 R^2 0.99822 Radj^2 0.99759 HQ -7.17108 SC -6.93068T 20 p 6 value probChow(2000:1) 0.2973 0.5948normality test 0.1066 0.9481

Benchmark Forecasts

The EPO Total Model

Dynamic analysis

Long-Run Effects

LAKT_US 1.4219 >>> Greater than Unity SE 0.4509 Same as Domestic

lrd31_eu 0.1111 >>> Elasticity < Domestic

SE 0.0611

LGDP3_EU -2.7397

SE 1.0579

Constant 3.6432

SE 2.2689

Benchmark Forecasts

The Forecasts

The aggregate and domestic models were solved dynamically in a stochastic simulation.

1000 repetitions Gauss-Seidel Method The models were fit through 1998 and then used to

forecast until 2002 2003 Below the Aggregate model and European Domestic

model results are presented graphically as an example. Actual and Forecasts with Confidence Intervals Percent Deviations from Actual

Benchmark Forecasts

45000

50000

55000

60000

65000

70000

75000

1997 1998 1999 2000 2001 2002 2003

F_EPF_EP (Baseline Mean)

F_EP_0MHF_EP_0ML

Actual and Forecasts from the Aggregate Total Model

Benchmark Forecasts

108000

112000

116000

120000

124000

1997 1998 1999 2000 2001 2002 2003

FDOM_EUFDOM_EU (Baseline Mean)

FDOM_EU_0MHFDOM_EU_0ML

Actual and Forecasts from the Aggregate Domestic Model

Benchmark Forecasts

-1.6

-1.2

-0.8

-0.4

0.0

0.4

0.8

1.2

1997 1998 1999 2000 2001 2002 2003

Total EPO

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1997 1998 1999 2000 2001 2002 2003

Domestic EPO

Percent Deviations from Aggregate Model

Benchmark Forecasts

Summary

This paper presents preliminary results on forecasting patent applications at the European Patent Office using annual data.

An Aggregate (top-down) Model and a Regional (bottom up) Model are developed.

The models are econometric and based on endogenous growth theory.

The results suggest this is a promising approach to forecasting patent applications at the EPO and useful for decision making.